Search Results for author:
Found 12 papers, 6 papers with code
Text-Video Retrieval with Disentangled Conceptualization and Set-to-Set Alignment
In this paper, we propose the Disentangled Conceptualization and Set-to-set Alignment (DiCoSA) to simulate the conceptualizing and reasoning process of human beings.
TG-VQA: Ternary Game of Video Question Answering
Video question answering aims at answering a question about the video content by reasoning the alignment semantics within them.
Multi-granularity Interaction Simulation for Unsupervised Interactive Segmentation
Interactive segmentation enables users to segment as needed by providing cues of objects, which introduces human-computer interaction for many fields, such as image editing and medical image analysis.
LaPE: Layer-adaptive Position Embedding for Vision Transformers with Independent Layer Normalization
A typical way to introduce position information is adding the absolute Position Embedding (PE) to patch embedding before entering VTs.
Position Embedding Needs an Independent Layer Normalization
By analyzing the input and output of each encoder layer in VTs using reparameterization and visualization, we find that the default PE joining method (simply adding the PE and patch embedding together) operates the same affine transformation to token embedding and PE, which limits the expressiveness of PE and hence constrains the performance of VTs.
Fuzzy Positive Learning for Semi-supervised Semantic Segmentation
Semi-supervised learning (SSL) essentially pursues class boundary exploration with less dependence on human annotations.
ACSeg: Adaptive Conceptualization for Unsupervised Semantic Segmentation
Recently, self-supervised large-scale visual pre-training models have shown great promise in representing pixel-level semantic relationships, significantly promoting the development of unsupervised dense prediction tasks, e. g., unsupervised semantic segmentation (USS).
Locality Guidance for Improving Vision Transformers on Tiny Datasets
Therefore, our locality guidance approach is very simple and efficient, and can serve as a basic performance enhancement method for VTs on tiny datasets.
$L_2$BN: Enhancing Batch Normalization by Equalizing the $L_2$ Norms of Features
In this paper, we analyze batch normalization from the perspective of discriminability and find the disadvantages ignored by previous studies: the difference in $l_2$ norms of sample features can hinder batch normalization from obtaining more distinguished inter-class features and more compact intra-class features.
DPR-CAE: Capsule Autoencoder with Dynamic Part Representation for Image Parsing
Parsing an image into a hierarchy of objects, parts, and relations is important and also challenging in many computer vision tasks.
MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal Angles
Extensive experiments demonstrate that MMA regularization is able to enhance the generalization ability of various modern models and achieves considerable performance improvements on CIFAR100 and TinyImageNet datasets.
PR Product: A Substitute for Inner Product in Neural Networks
In this paper, we analyze the inner product of weight vector w and data vector x in neural networks from the perspective of vector orthogonal decomposition and prove that the direction gradient of w decreases with the angle between them close to 0 or {\pi}.
